Necati Cihan Camgöz

Artificial Intelligence · 2016–2021

A researcher in the Science corpus: 6 papers; trusts 540 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 165 citations into it
• Computer Vision and Pattern Recognition — 100 citations into it; #11,760 of 21,575 by trust here, #11,721 by citations
• Model Architectures — 45 citations into it
• Natural Language Processing — 42 citations into it
• Causal Inference & Discovery — 39 citations into it
• Human-Computer Interaction — 33 citations into it; #902 of 2,253 by trust here, #837 by citations
• Statistics and Probability — 28 citations into it
• Signal Processing — 27 citations into it

Whom Necati Cihan Camgöz trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Yoshua Bengio
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Alex Graves
5. Ilya Sutskever
6. Yann LeCun
7. Alex Krizhevsky
8. Kaiming He
9. J. Urgen Schmidhuber
10. Christian Szegedy

Who trusts Necati Cihan Camgöz overall

1. Oscar Koller
2. Hamid Reza Vaezi Joze
3. Hao Zhou
4. Pichao Wang
5. Yifan Zhang
6. Xiujuan Chai
7. Richard Bowden
8. Simon Hadfield
9. Shiry Ginosar
10. Wen-gang Zhou

Papers from Necati Cihan Camgöz's perspective

Ordered by how strongly the researchers Necati Cihan Camgöz trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Sign Language Transformers: Joint End-to-End Sign Language Recognition and Translation (2020) — 771 citations
• Neural Sign Language Translation (2018) — 863 citations
• Using Convolutional 3D Neural Networks for User-independent continuous gesture recognition (2016) — 98 citations
• SubUNets: End-to-End Hand Shape and Continuous Sign Language Recognition (2017) — 335 citations
• D’ya Like DAGs? A Survey on Structure Learning and Causal Discovery (2021) — 400 citations
• Weakly Supervised Learning with Multi-Stream CNN-LSTM-HMMs to Discover Sequential Parallelism in Sign Language Videos (2020) — 365 citations